Investigating Temporal Features of Carotid Intima-Media Thickness from Ultrasound Imaging with Recurrent Neural Networks

Measuring carotid intima-media thickness (cIMT) of the Common Carotid Artery (CCA) via B-mode ultrasound imaging is a non-invasive yet effective way to monitor and assess cardiovascular risk. Recent studies using Convolutional Neural Networks (CNNs) to automate the process have mainly focused on the detection of regions of interest (ROI) in single frame images collected at fixed timepoints and have not exploited the temporal information captured in ultrasound imaging. This paper presents a novel framework to investigate the temporal features of cIMT, in which Recurrent Neural Networks (RNN) were deployed for ROI detection using consecutive frames from ultrasound imaging.
The cIMT time series can be formed from estimates of cIMT in each frame of an ultrasound scan, from which additional information (such as min, max, mean, and frequency) on cIMT time series can be extracted. Results from evaluation show the best performance for ROI detection improved 4.75% by RNN compared to CNN-based methods. Furthermore, the heart rate estimated from the cIMT time series for seven patients was highly correlated with the patient’s clinical records, which suggests the potential application of the cIMT time series and related features for clinical studies in the future.